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131 lines (117 loc) · 4 KB
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/**
* Embedder tests
*
* Tests the ONNX Runtime embedder contract:
* output shape, determinism, differentiation, normalization.
* Requires ONNX model files at the configured model_dir.
*/
#include "config.h"
#include "embedder.h"
#include <cassert>
#include <cmath>
#include <filesystem>
#include <iostream>
#include <numeric>
#include <print>
void test_output_shape(ragger::Embedder& emb) {
std::println(" test_output_shape...");
auto vec = emb.encode("test text");
assert(vec.size() == 384);
assert(emb.dimensions() == 384);
std::println(" OK");
}
void test_deterministic(ragger::Embedder& emb) {
std::println(" test_deterministic...");
auto v1 = emb.encode("hello world");
auto v2 = emb.encode("hello world");
assert(v1.size() == v2.size());
for (size_t i = 0; i < v1.size(); ++i) {
assert(v1[i] == v2[i]);
}
std::println(" OK");
}
void test_different_texts_different_vectors(ragger::Embedder& emb) {
std::println(" test_different_texts_different_vectors...");
auto v1 = emb.encode("hello world");
auto v2 = emb.encode("goodbye moon");
// At least some dimensions must differ
bool any_different = false;
for (size_t i = 0; i < v1.size(); ++i) {
if (std::fabs(v1[i] - v2[i]) > 1e-6f) {
any_different = true;
break;
}
}
assert(any_different);
std::println(" OK");
}
void test_unit_normalized(ragger::Embedder& emb) {
std::println(" test_unit_normalized...");
auto vec = emb.encode("test normalization");
float sum_sq = 0.0f;
for (float v : vec) sum_sq += v * v;
float norm = std::sqrt(sum_sq);
assert(std::fabs(norm - 1.0f) < 1e-4f);
std::println(" OK");
}
void test_empty_string(ragger::Embedder& emb) {
std::println(" test_empty_string...");
auto vec = emb.encode("");
assert(vec.size() == 384);
// Should still produce a valid vector (not NaN/inf)
for (float v : vec) {
assert(!std::isnan(v));
assert(!std::isinf(v));
}
std::println(" OK");
}
void test_long_text(ragger::Embedder& emb) {
std::println(" test_long_text...");
// MiniLM has 512 token limit; verify it handles longer input gracefully
std::string long_text;
for (int i = 0; i < 1000; ++i) long_text += "word ";
auto vec = emb.encode(long_text);
assert(vec.size() == 384);
float sum_sq = 0.0f;
for (float v : vec) sum_sq += v * v;
float norm = std::sqrt(sum_sq);
assert(std::fabs(norm - 1.0f) < 1e-4f);
std::println(" OK");
}
void test_semantic_similarity(ragger::Embedder& emb) {
std::println(" test_semantic_similarity...");
auto v_cat = emb.encode("The cat sat on the mat");
auto v_kitten = emb.encode("A kitten rested on the rug");
auto v_sql = emb.encode("SELECT * FROM users WHERE id = 1");
// Cosine similarity (vectors are unit-normalized, so dot product = cosine)
auto dot = [](const std::vector<float>& a, const std::vector<float>& b) {
float sum = 0.0f;
for (size_t i = 0; i < a.size(); ++i) sum += a[i] * b[i];
return sum;
};
float sim_related = dot(v_cat, v_kitten);
float sim_unrelated = dot(v_cat, v_sql);
// Related sentences should be more similar than unrelated ones
assert(sim_related > sim_unrelated);
std::println(" OK");
}
int main() {
std::println("Running embedder tests:");
ragger::init_config();
auto cfg = ragger::config();
auto model_dir = cfg.resolved_model_dir();
if (!std::filesystem::exists(model_dir + "/model.onnx") && !std::filesystem::exists(model_dir + "/onnx/model.onnx")) {
std::cerr << "Skipping embedder tests: model not found at " << model_dir << "\n";
return 0;
}
ragger::Embedder emb(model_dir);
test_output_shape(emb);
test_deterministic(emb);
test_different_texts_different_vectors(emb);
test_unit_normalized(emb);
test_empty_string(emb);
test_long_text(emb);
test_semantic_similarity(emb);
std::println("test_embedder: all passed");
return 0;
}